Time domain modeling and classification of wheat impact acoustic signal
小麦碰撞声信号时域建模与分类

Analyzing the wheat impact acoustic is help to recognize the damaged wheat kernels. This paper extracted three types of wheat impact acoustic signal, and analysed the time domain characteristics of all the acoustic, established a suitable fitting model, and got the statistical parameters of SSE, the peak amplitude and other four time domain characteristics, and used BP neural network to class the wheat kernels. The experiment finds that the time domain characteristics of undamaged wheat kernels, the insect damaged kernels and the moldy kernels are different, and have achieved a better recognition rate. The application results show that the selection of appropriate mathematical model can fit the wheat impact acoustic signal better, distinguish undamaged wheat kernels and damaged wheat kernels successfully.